Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

Abstract

Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN), including ordinary RNN, long shortterm memory (LSTM), and gated recurrent unit (GRU), are individually constructed. Thirdly, ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction. The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets. Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures.

A remaining useful life prediction method based on PSR-former

“…In order to compare the methods in this paper with those adopted in the current field, the algorithms are classified into two categories: the method based on deep learning and the method based on traditional machine learning. In deep learning methods, Transformer 29 , RNN 30 , LSTM 31 , GRU 32 , 1D-CNN 33 , and CNN combined with LSTM 34 were chosen under the same data settings as proposed method in this paper to analyze and they were all connected in three layers whose hidden dimension is 14 and 64 to be as close as possible to the PSR-former model. The other parameters of the networks such as batch size and learning rate were adjusted to the best.…”

Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities

“…Aircraft engines are complex and require regular maintenance, making up 35-40% of the total aircraft maintenance expenses from an operator48 . Turbofan engines can contain large suites of sensors that record values such as fan inlet temperature and pressure, and physical fan speed49 . C-MAPSS generated datasets have been found to be used most frequently in publications, particularly the datasets released for the PHM 2008 data challenge,24 which has cemented itself as an established benchmark for new approaches.State-of-the-art reviews have already been conducted investigating aircraft engines.…”